On the kernel of actions on asymptotic cones

Any finitely generated group G acts on its asymptotic cones in natural ways. The purpose of this paper is to calculate the kernel of such actions. First, we show that, when G is acylindrically hyperbolic, the kernel of the natural action on every asymptotic cone coincides with the unique maximal finite normal subgroup K ( G ) K(G) of G. Secondly, we use this equivalence to interpret the kernel of the actions on asymptotic cones as the kernel of the actions on many spaces at "infinity". For instance, if G curved right arrow M G\curvearrowright M is a non-elementary convergence group, then we show that the kernel of actions on the limit set L ( G ) L(G) coincides with the kernel of the action on asymptotic cones. Similar results can also be established for the non-trivial Floyd boundary and the CAT ( 0 ) groups with the visual boundary, contracting boundary, and sublinearly Morse boundary. Additionally, the results are extended to another action on asymptotic cones, called Paulin's construction. In the last section, we calculate the kernel on asymptotic cones for various groups, and as an application, we show that the cardinality of the kernel can determine whether the group admits a non-elementary action under some mild assumptions.

Robotic flexible ureteroscopy system, Zamenix R, demonstrates efficacy and safety in initial clinical evaluation for retrograde intrarenal surgery

This initial clinical trial evaluated the efficacy and safety of the retrograde intrarenal surgery using the robotic flexible ureteroscopy system, Zamenix R, in a multi-center, prospective, single-arm study. A total of 47 adult Korean patients with one or more kidney stones, ranging in maximal size from 5 to 30 mm, were recruited, and 46 patients were included in the analysis. The median age of patients was 57.50 [IQR 48.25-63.00] years, with a median of 1.00 [IQR 1.00-2.00] stones per patient and a median maximal size of 13.70 [IQR, 10.00-16.00] mm and a volume of 349.65 [IQR 201.60-704.10] mm(3). The stone-free rates were 93.48% (< 4 mm), 71.74% (< 2 mm), and 56.52% (zero stone), based on each respective stone-free definition, with no conversions to conventional surgery. The median operative time was 91.50 [IQR 64.25-113.75] minutes, with a median console time of 71.00 [IQR 39.25-92.75] minutes. Ureteral injuries occurred in 17.39% of cases, including 6.52% Grade I and 10.87% Grade II injuries, all related to manual insertion of a ureteral access sheath. The postoperative complication rate was 6.52%, with all cases being Grade II urinary tract infections. Laser ablation speed was higher for larger stones. The operators reported low levels of musculoskeletal fatigue and numbness. The results demonstrated the efficacy and safety of the robot-assisted flexible ureteroscopic surgery using Zamenix R. Further investigation is required, including a comparison with the standard of care and a larger sample size.

Can AV crash datasets provide more insight if missing information is supplemented? Employing Generative Adversarial Imputation Networks to Tackle Data Quality Issues

The growing prevalence of autonomous vehicles (AVs) offers new opportunities for enhancing traffic efficiency. However, AVs still face significant challenges that impact their safety and effectiveness in preventing accidents. Real-world operational data is therefore essential to identifying the factors contributing to AV crashes. Despite this, the analysis of AV crashes is still hampered by a lack of data, missing information, and underreporting, which negatively impacts its accuracy and comprehensiveness. To address this challenge, a method based on Generative Adversarial Networks (GANs) was used for data imputation, leveraging their advantage in handling heterogeneous data. An evaluation of the performance of our proposed data imputation approach was performed by comparing it with two established methods, namely conventional case deletion and Random Forest (RF) imputation. Synthetic data obtained from these three methods were modelled using the random parameters logit model with heterogeneity in means. Data from the California Department of Motor Vehicles (DMV) and the National Highway Traffic Safety Administration (NHTSA) covering 2021-2023 were used. Our results showed that the model based on Generative Adversarial Imputation Networks (GAIN)- processing data outperformed other candidate methods in terms of fitting, predictive accuracy, and factor interpretation. Our results suggest that factors including speed limit, roadway types, head-on crashes, and takeover of ADAS-equipped vehicles are positively associated with serious injury crashes. On the other hand, ADS engagement and crashes with fixed objects exhibit a negative association with serious injury crashes. Additionally, heterogeneous effects of posted speed limits and ADS engagement on AV crash severity were captured to provide a deeper insight into implications.

Conspiratorial thinking in the workplace: how it happens and why it matters

PurposeAlthough belief in conspiracy theories has been researched since the 1970s, specific research on conspiratorial thinking in the workplace is scarce. Conspiratorial thinking could be fostered among employees in workplaces because of unequal power relations resulting from the organizational hierarchy. This study examines workplace conspiracy attribution (WCA) as employees' attribution of problematic events in the workplace as being plotted by powerful actors within their organizations and tests its antecedents and consequences.Design/methodology/approachA survey dataset collected from employees in South Korea (N = 600) was used. This study tested three variables (i.e. two-way communication, employee-organization relationship quality, and perceived ethical orientation) as antecedent conditions of WCA and two outcome variables (i.e. turnover intention and whistleblowing potential) as consequences.FindingsPerceived ethical orientation mediates the relationship between two-way communication and WCA. WCA was found to be positively associated with turnover intention and whistleblowing potential.Originality/valueThis study adopts a public relations lens to understand the significant roles of WCA in reducing turnover intention and whistleblowing potential. It expands existing knowledge of the significance of power and power disparities in organizations.

Attribute-guided Relevance Propagation for interpreting image classifier based on Deep Neural Networks

Deep learning techniques have emerged as powerful tools for addressing complex and varied problems, achieving remarkable success across numerous AI domains. Despite their effectiveness, the inherent complexity of deep learning models makes them considered black boxes, reducing their interpretability and reliability. To address this challenge, we propose a novel approach called Attribute-guided Relevance Propagation (ARP). ARP enhances the interpretability of deep learning models by learning attributes from specific layers within a pre-trained image classifier and integrating these attributes into saliency maps. This integration not only improves the saliency maps but also identifies and provides example images related to key regions reflected in the maps. We validate the efficacy of ARP through both quantitative and qualitative evaluations, employing widely recognized image classifiers such as ResNet-50 and ViT trained on the benchmark datasets.

3D POINT CLOUD-BASED DEEP LEARNING NEURAL NETWORK ACCELERATOR AND METHOD THEREFOR

본 발명은 3D 포인트 클라우드 기반 딥러닝 신경망 가속장치에 관한 것으로서, 뎁스 이미지를 입력받는 뎁스 이미지 입력부; 상기 뎁스 이미지로부터 도출된 뎁스 데이터를 저장하는 뎁스 데이터 저장부; 미리 설정된 제1 크기의 샘플링 윈도우 단위로 상기 뎁스 이미지를 샘플링하는 샘플링부; 상기 샘플링 결과에 의거하여, 미리 설정된 제2 크기의 그룹핑 윈도우를 생성한 후, 상기 그룹핑 윈도우별로 내부의 3D 포인트 데이터들을 그룹핑하는 그룹핑부; 및 상기 뎁스 이미지를 구성하는 3D 포인트 데이터들 각각의 채널방향 데이터들 중, 포인트 피처 데이터들 및 그룹 피처 데이터들을 분리하여 컨볼루션 연산한 후, 그 결과를 합산하여 최종 결과를 도출하는 컨볼루션 연산부를 포함한다. 따라서, 본 발명은 외부 메모리 접근량과 연산량을 크게 줄임으로써, 속도를 빠르게 할 수 있고, 희소 입력 및 출력 스키핑을 3D 포인트 클라우드 기반 딥러닝 신경망 가속에 적용하고, 중복되는 컨볼루션 연산 과정을 생략함으로써, 컨볼루션 연산을 가속화할 수 있는 장점이 있다

Autonomous process optimization of a tabletop CNC milling machine using computer vision and deep learning

CNC milling machines offer precision manufacturing across diverse materials but require off-machine quality checks and trial-and-error parameters selection. This study proposes a system that autonomously adjusts such parameters to improve surface quality and productivity. Composed of a deep learning-based monitoring apparatus capable of on-site surface roughness prediction with a 3.6 % mean error and a dataset of optimized parameters generated via multi-objective Bayesian optimization in only eleven attempts, it successfully conducted a fully autonomous trochoidal slotting operation, improving the final roughness by 36 %. The system, with further refinements, can be industrialized making autonomous machining accessible even to small and medium enterprises.

Nucleation-percolation transition in desiccation cracking of clay

Crack formation plays a critical role in both natural and engineered materials, influencing the structural integrity and failure of materials from soils to buildings. In this study, the clay desiccation cracking is observed to exhibit a transition of cracking mode from nucleation through avalanche to percolation. This cracking mode transition is dictated by the strain field disorder, which is deliberately regulated by changing the pore structure heterogeneity, with nucleation dominating at low disorder and percolation emerging at higher disorder levels. These transitions are captured by the fractal dimension of the sample-spanning crack, with '0.99 for nucleation, '1.72 for percolation, and values in between for avalanche, maintaining universality among the four types of clay. Additionally, the fractal dimension increases with strain field disorder in avalanche mode, which can be well captured by a logarithmic relation derived via the fractal tree model. Moreover, this logarithmic relation is universal among both the experimental data of the four types of clay and the numerical data of the classical fuse model. Our findings enhance the understanding of how material heterogeneity governs crack propagation, providing valuable insights for predicting and controlling fractures in natural and industrial processes.

펩타이드-MHC에 대한 T 세포 활성의 예측 방법 및 분석장치

MHC-ペプチドに対するT細胞活性の予測方法は、分析装置が患者の遺伝子データを入力される段階と、前記分析装置が前記遺伝子データを基準としてMHC(major histocompatibility complex)の第1アミノ酸配列および腫瘍細胞が生成する抗原の第2アミノ酸配列を識別する段階と、前記分析装置が1つのアミノ酸単位で前記第1アミノ酸配列と前記第2アミノ酸配列の間の相互関係を示すマトリクスを生成する段階と、前記分析装置が前記マトリクスを学習された神経網モデルに入力し、前記MHCおよび前記抗原の結合によってT細胞がサイトカイン(cytokine)を臨界値以上分泌するか否かを判断する段階と、を含む。 【選択図】図